Chinese herbal medicine for patients living with HIV in Guangxi province, China: A propensity score matching analysis of real-world data

Background From 2004 onwards, the Chinese government has freely offered complimentary Chinese herbal medicine (CHM) to Chinese HIV/AIDS patients, alongside the prescribed first line therapy of highly active antiretroviral therapy (HAART). Thus, we aimed to explore the effectiveness and safety of CHM for patients with HIV/AIDS. Methods The data from the Guangxi pilot database and antiviral treatment sites database have been respectively developed into two datasets in this prospective cohort real-world study, the CHM combined HAART group (the integrated group) and the HAART group. A 1:1 propensity score matching (PSM) was performed and the longitudinal data were analyzed using a generalized estimating equation (GEE) model with an autocorrelation matrix and log link function attached to the Gamma distribution. Results A final sample of 629 patients, 455 and 174 in the integrated group and HAART group respectively, were obtained from the full dataset. As covariates for PSM, gender, age, baseline CD4+ and CD4+/ CD8+ were assessed based on the results of the logistic regression analyses. Following PSM, 166 pairs from the full dataset were matched successfully, with 98 pairs in the baseline CD4+ > 200 subgroup, and 55 pairs in the baseline CD4+ ≤ 200 subgroup. In the full dataset, HAART group achieved higher CD4+ count (OR = 1.119, 95%CI [1.018, 1.230]) and CD4+/CD8+ ratio (OR = 1.168, 95%CI [1.045, 1.305]) than the integrated group, so did in the CD4+ > 200 subgroup. For the CD4+ ≤ 200 subgroup, the CD4+ (OR = 0.825, 95%CI [0.694, 0.980]) and CD4+/CD8+ (OR = 0.826, 95%CI [0.684, 0.997]) of the integrated group were higher than those of the HAART group. The safety outcomes showed that there were no significant differences in BUN, ALT and AST levels between the groups but Cr showed significantly higher levels in HAART groups of all three datasets. Conclusions Compared to HAART alone, CHMs combined with HAART had better effects in improving the immune function of HIV/AIDS in patients with baseline CD4+ count ≤ 200. The results of the two subgroups are in opposite directions, and chance does not explain the apparent subgroup effect. A study with larger sample size and longer follow-up period is warranted in order to increase study credibility.


Background
Acquired immunodeficiency syndrome (AIDS) is a chronic and fatal infectious disease caused by the human immunodeficiency virus (HIV).HIV destroys the white blood cells called CD4 + cells, weakening a person's immunity against opportunistic infections, such as tuberculosis and fungal infections, severe bacterial infections and some cancers [1].According to the Joint United Nations Programme on HIV/AIDS (UNAIDS), HIV/AIDS has driven 37.7 million people to be affected by the end of 2020, with 1.5 million newly HIV infected in that year.About 27.5 million people are now receiving antiretroviral therapy.which is indicated in cases of HIV [2].Currently, there are six classes of AIDS agents in the world, namely nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), integrase inhibitors (INSTIs), fusion inhibitors (FIs) and CCR5 inhibitors [3].Long-term use of NRTI agents could be attributed to producing adverse reactions of hyperlactatemia and lactic acidosis, neuropathy, pancreatitis and lipoatrophy [4] .The most commonly used NNRTIs, efavirenz is known to have a significant side-effect profile that includes neuropsychiatric toxicity, rash, hyperlipidemia and elevated transaminases [5,6].According to the world health organization's HIV Drug Resistance Report in 2021, more and more nations were reaching the 10% threshold of pretreatment HIV drug resistance (HIVDR) to NNRTIs and it was found up to 3 times more likely in people who had previous exposure to antiretroviral agents [7].PIs may likewise elicit side effects encompassing metabolic abnormalities including dyslipidemia (primarily triglycerides), insulin resistance, hyperglycemia, and lipodystrophy [8] .
Chinese herbal medicine (CHM) was first used to treat HIV-infected people in 1987, when traditional Chinese medicine (TCM) practitioners from China provided medical assistance in Tanzania, Africa [9].Given its promising effectiveness and high safety profile, CHM has been used in the treatment of HIV/AIDS for more than 30 years as an alternative and complementary therapy to highly active antiretroviral therapy (HAART) [10].The application of TCM has also been proven to significantly improve patients' clinical symptoms and signs, improve their working capacity and quality of life, safeguard their immune system, and postpone the onset of AIDS [11][12][13][14].Additionally, the synergistic administration of CHM and antiviral agents can reduce some adverse reactions to antiviral agents [11].A pilot project in China, the National Free CHM HIV/AIDS Treatment Program (NFCHMP), was launched in 2004 and extended rapidly.NFCHMP was supported by the State Administration of Traditional Chinese Medicine (SATCHM) and the Ministry of Finance, and the program has provided free TCM treatment to tens of thousands of HIV/AIDS patients.
It is apparent that real-world studies (RWSs) are not constrained by the small sample size or the strict inclusion criteria, such as the exclusion of children or the elderly, as are randomized controlled trials (RCTs).As a result, it can contribute to a broad evaluation of treatment modes and external effectiveness [15].A number of RWSs have been conducted on HIV.For instance, a multicentral RWS by Santinelli et al. assessed the real-life effectiveness, tolerability, and safety of long-term Raltegravir use in elderly HIV infected patients [16].Similarly, Okoli et al described the actual use and effectiveness of Dolutegravir-based regimens in HIV patients treated in the United Kingdom [17].A retrospective cohort study was conducted in Henan province on basis of the NFCHMP database and it demonstrated that CHM could decrease the disease progression, reduce the mortality of people living with HIV, and improve life expectancy.However, the predominant limitation was that they chose the contemporaneous world mortality rate as a comparison [18].A 7-year observational study indicated long-term utilisation of CHM could keep up or impede the pace of CD4 + cell counts declining.However, this study did not address bias, potential confounders and the possibility that results may have occurred by chance [19].RWSs employing TCM to treat HIV are, whereas, limited in small sample size, do not address confounding factors, or ignored individual disease progression.
RWS, on the other hand, may be accompanied by more confounding factors than RCTs.Thus, propensity score-based approaches have been developed to reduce or remove the factors [20].The propensity score represents the probability of assigning treatment conditions on observed baseline attributes.Furthermore, Liang and Zeger proposed the generalized estimating equation (GEE) to analyze real-world data (RWD), which was developed just on the basis of the generalized linear model (GLM) and enhance GLM to accommodate the modelling of correlated data.GEE is appropriate for complete data or missing data at random [21].Therefore, we aimed to analyze the longitudinal data using propensity score matching (PSM) and GEE to explore the effectiveness and safety of CHM for patients with HIV/AIDS.

Ethics approval and consent to participate
This study was approved by the ethics committee of the Beijing University of Chinese Medicine (BZZYYDX-LL20160215).

Data source
The ethics committee of the Beijing University of Chinese Medicine approved this study before data collection began (BZZYYDX-LL20160215).The prospective cohort study was based on two registration databases, the Guangxi pilot database of the NFCHMP (hereinafter referred to as Guangxi pilot database) and the antiviral treatment site database of Ruikang Hospital affiliated with the Guangxi University of Traditional Chinese Medicine (hereinafter referred to as antiviral treatment sites database).The study participants provided their written informed consent and permitted the use of their medical information.

Participants
The real-world data was composed of two sets, namely the CHM combined with the HAART group (integrated group for short) and the HAART group.The participants in the integrated group were sourced from the Guangxi pilot database and those in the HAART group were sourced from the antiviral treatment sites database.Over the course of 36 months, all participants were followed up every three months.
Eligible participants were those diagnosed with HIV/AIDS and receiving HAART treatment between 2004 and 2016.A complete set of included case data should be provided with all necessary information.Participants were excluded if they did not have baseline characteristics (gender, age, marital status, possible route of infection) or CD4 + baseline data.In the case of participants with all follow-up data missing within 36 months, they were excluded from the study.

Outcomes
Over a period of 36 months, the primary outcome was CD

Data analysis
Data processing and data analysis were undertaken using Excel 2016 and SPSS 22.0, respectively.For normally distributed quantitative data (CD4 + and CD8 + ), we used the Pauta criterion (values beyond � X ± 3S) to detect outliers.After verification, outliers and anomalies that represent accurate values were still included in the analysis.The continuous variables were statistically described by means and standard deviation, median and 95% confidence intervals (CIs), and were compared between groups using a t-test or Wilcoxon rank-sum test.Categorical variables were described by constituent ratios, and intergroup comparisons were conducted using the chi-square test or Fisher exact probability method.To explore the covariates associated with PSM, a logistic regression model was applied.The PSM was carried out using the R plug-in in SPSS.In the GEE model, an autocorrelation matrix of an autoregressive AR (1) process was selected in the study for dealing with data at different time points.Statistically significant was determined by P < 0.05.
In the logistic regression model, the outcome variable was the CD 4 + cell count change.
Using the relative magnitude of the difference between the CD 4 + cell count at the last followup visit and baseline for each patient, and the mean of these differences for all patients, the change in CD 4 + cell count has been expressed.In cases where the difference was greater than the mean of the differences (dominant population), 1 was used, while in cases where the difference was less than the mean of the differences (inferior population), 0 was used.Let a binary dependent variable be set as Y, then: ALT and AST.Upon considering the 11 independent variables, a logistic regression model [22], was used to calculate the probability of obtaining positive results.
A propensity score (PS) is defined as the conditional probability of assigning a research object to the treatment group when multiple covariates are present [23].The baseline characteristic variables selected in this study were used as the matching factors, and the 1:1 matching was performed based on the principles of nearest neighbour matching and calliper matching (calliper value: 0.03) [24].In addition, the value of CD 4 + � 200 was of important clinical significance and was considered to be in the AIDS stage.Hence, the PSM was conducted within two subgroups respectively, baseline CD 4 + count > 200 and baseline CD 4 + count � 200.

Sample characteristics
The identification and selection process of the study sample is shown in Fig

CD4+ cell count
The results of CD 4 + cell count over time in patients before PSM are presented in S3 Table.
After PSM, Table 2 illustrates the changes in CD 4 + among AIDS patients based on their treatment every three months during the follow-up period.In the full dataset, a significant difference was observed between the groups at 9, 15, 18, 30 and 33 months of follow-up and the CD 4 + levels were all higher in the WM group than that was in the integrated group.According to the statistical significance in the baseline CD 4 + > 200 subgroup, detected from 3 to 33 months of follow-up, the CD 4 + levels in the HAART group were all higher than that in the integrated group.In the subgroup with baseline CD 4 + � 200, CD 4 + was significantly different at the 3rd, 9th and 15th-month follow-up, with higher levels in the integrated group.subgroup, the HAART group maintained higher CD4 + mean levels than the integrated group during the 36-month follow-up period.During the first 6 months of follow-up of the full dataset, CD4 + mean levels were higher in the integrated group than in the HAART group and after 6 months, they remained higher in the HAART group.As for the baseline CD 4 + � 200 subgroup, higher CD4 + mean levels were observed in the integrated group than in the HAART group during the 36-month follow-up period, except for the 18 th and 30 th ~36 th months.All six groups experienced rapid CD4 + increases during the first three months.After three months, the integrated group displayed an overall decrease, whereas the HAART group showed an accumulated increase.

CD4 + /CD8 + cell ratio
The CD4 + /CD8 + ratios of patients before and after PSM are respectively presented in S4

Analysis results based on generalized estimating equation model
The GEE model was used to analyse the data after PSM, shown in Table 4, with the integrated group serving as the control group.On the basis of the full dataset, CD ) than in the integrated group, while the baseline CD4 + � 200 subgroup showed the opposite, the integrated group revealing higher CD4 + (OR 0.825, 95%CI [0.694, 0.980]) and CD4 + /CD8 + (OR 0.826, 95%CI [0.684, 0.997]).As far as safety outcomes are concerned, there were no statistically significant differences between the integrated and the HAART groups in the three datasets in terms of BUN, ALT and AST.Cr level was found significantly higher in the HAART group in all three datasets.

Discussion
We analyzed the real-world longitudinal data to explore the effectiveness and safety of CHM for patients with HIV/AIDS.The patients were followed up for 36 months.A 1:1 PSM was performed to balance the baseline and eliminate confounding factors.The longitudinal data were .997] of the integrated group were higher than those of the HAART group.The safety outcomes showed that there were no significant differences in BUN, ALT and AST levels between the groups but Cr showed significantly higher levels in HAART groups of all three datasets.In spite of the high external validity of real-world data, there are a number of confounding factors that can make causal inferences less accurate.To improve the internal validity of inferences, matching methods are often applied to real-world data.To improve the accuracy of the results of this study, we use PSM to determine the baseline and to balance both the internal and external validity of real-world data.Meanwhile, using CD4 + as the outcome variable, we The limitation of this study mainly lies in the high rate of lost follow-up of data.Although an appropriate analytical model is adopted for processing, the high rate of lost follow-up will have a certain impact on the accuracy of the results.In addition, PSM can only make equilibrium adjustments for known and measurable covariables, but cannot control the effect of unknown or unmeasured covariables on the outcome effect.Only when all covariables are known and measurable can the unbiased estimation of outcome effects be truly realized.
The results of the subgroup (baseline CD4 + > 200) showed that the HAART group was superior to the integrated group to improve patients' immune function, whereas the full dataset and the baseline CD4 + � 200 subgroup revealed that the integrated group was more beneficial.The result is consistent with a previous cohort study we conducted without PSM, showing that the CD4 + counts of the integrated group remained significantly lower than those of the HAART group in the first 3 years [25].However, the results of the two subgroups were in opposite directions, showing qualitative differences, which is relatively rare.Chance alone is unlikely to account for significant subgroup effects, which may not be genuine [26].Data from a single study can only generate hypotheses about differences between subgroups.To enhance credibility, it is essential to replicate such findings through repeated studies.

Conclusion
The results of the study show that after three years of treatment, the difference between CHMs combined with antiviral therapy and the use of antiviral therapy alone in improving the immune function of HIV infection and AIDS patients has no clinical significance.The results of the two subgroups are in opposite directions, and chance does not explain the apparent subgroup effect.A study with a larger sample size and longer follow-up period is warranted in order to increase study credibility.

Fig 2
Fig 2 depict changes in CD 4 + longitudinal data after PSM.Within the baseline CD4 + > 200

Fig 1 .
Fig 1. Patients flow diagram for the prospective cohort study.https://doi.org/10.1371/journal.pone.0304332.g001 As possible covariates of PSM, the following variables may affect the immune function of HIV/ AIDS patients: gender, age, marital status, possible route of infection, baseline CD 4 dominant population ðpositive resultÞ 0; inferior population ðnegative resultÞ ( + T-cell ratio, and baseline level of Cr, BUN,

Table and Table 3 .
Statistical analysis of the full dataset revealed that the CD4 + /CD8 + ratio between the two groups was statistically significant at 15, 18 and 21 months, and the HAART group had a higher ratio than the integrated group.In terms of the CD 4 + > 200 subgroup, the HAART group also demonstrated statistically significant higher CD4 + /CD8 + ratios than the integrated group among the 12 visits of follow-up, with an exception of the 33rd month.
+ > 200 subgroup, the ratio maintained higher levels in the HAART group throughout the study follow-up, whereas the integrated group remained at higher levels within the CD 4 + � 200 subgroup over the entire 36 months.All six groups experienced CD4 + /CD8 + ratio increases during the first 6 months.

Table 3 .
(Continued) � 200 subgroups.In the GEE model, repeated measures are fully taken into account.The GEE is also capable of analyzing a variety of types of outcome variables, as well as processing data with missing data with different observation times and time intervals for observed objects.

Table 4 . Analysis of longitudinal data for HIV/AIDS patients based on the generalized estimating equation model.
Cr refers to creatinine; BUN refers to blood urea nitrogen; ALT refers to alanine transaminase; AST refers to aspartate aminotransferase https://doi.org/10.1371/journal.pone.0304332.t004